Fed-AdScale: A Distributed Edge-Cloud Infrastructure for Social Commerce via Privacy-Preserving LLM Inference and Trusted Execution
Abstract
The digital economy is currently witnessing a convergence of social media dynamics and electronic commerce, a phenomenon termed social commerce that relies heavily on personalized, context-aware interactions. However, the centralization of user data required for Large Language Model (LLM) inference creates significant privacy risks and regulatory hurdles. This paper proposes Fed-AdScale, a high-throughput distributed edge-cloud infrastructure designed to facilitate privacy-preserving LLM inference within social commerce ecosystems. Fed-AdScale leverages a multi-tier architecture that distributes computational workloads between resource-constrained edge devices and robust cloud environments using Trusted Execution Environments (TEEs) to ensure data confidentiality and model integrity. By implementing a novel hierarchical orchestration layer, the system manages the inherent trade-offs between inference latency, model accuracy, and communication overhead. We provide an exhaustive analysis of the system’s structural design, emphasizing its robustness against adversarial attacks and its alignment with emerging global data governance policies. The discussion extends to the socio-technical implications of decentralized AI, including environmental sustainability through localized compute optimization and the promotion of algorithmic fairness via non-siloed data processing. By synthesizing advancements in hardware-assisted security and federated systems engineering, Fed-AdScale provides a scalable blueprint for the next generation of social commerce platforms that prioritize both user agency and commercial performance.
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